Optimization and Performance Evaluation of Deep Learning Algorithm in Medical Image Processing

Jingbo Zhang; Lingxi Xiao; Yuwei Zhang; Jiatao Lai; Yutian Yang

doi:10.54097/de0qx980

Authors

Jingbo Zhang
Lingxi Xiao
Yuwei Zhang
Jiatao Lai
Yutian Yang

DOI:

https://doi.org/10.54097/de0qx980

Keywords:

Deep Learning, Performance Evaluation, Medical Image

Abstract

In this paper, the optimization and performance evaluation of deep learning algorithm in medical image processing are studied. Firstly, the paper introduces the importance and challenges of medical image processing, and expounds the application prospect of deep learning in this field. Subsequently, this paper discusses the optimization methods of deep learning algorithm in detail, including model structure design, data preprocessing, super parameter adjustment and so on. In terms of performance evaluation, this study selected classic models such as U-Net, DeepLab and DenseNet, and compared them with ROC curve and AUC value to evaluate their predictive ability in medical image classification. The results show that the DenseNet model shows high performance in prediction accuracy, while the performance of U-Net and DeepLab models is slightly average. Finally, the advantages and disadvantages of each model are analyzed, and the future research direction is prospected. This study is of great significance to promote the development and application of medical image processing technology, and provides important theoretical and technical support for medical diagnosis and treatment.

Downloads

Download data is not yet available.

References

Zhang, X. , Chen, Z. , Gao, J. , Huang, W. , Li, P. , & Zhang, J. (2022). A two-stage deep transfer learning model and its application for medical image processing in traditional chinese medicine. Knowledge-based systems, 2022(5), 239.

Rajendran, T. , Sridhar, K. P. , Manimurugan, S. , & Deepa, S. (2019). Advanced algorithms for medical image processing. The Open Biomedical Engineering Journal, 13(1), 102-102.

Rajagopalan, S. , Poori, S. , Narasimhan, M. , Rethinam, S. , Kuppusamy, C. V. , & Balasubramanian, R. , et al. (2020). Chua's diode and strange attractor: a three-layer hardware software co-design for medical image confidentiality. IET Image Processing, 14(7), 1354-1365.

Yan, X., Xiao, M., Wang, W., Li, Y., & Zhang, F. (2024). A Self-Guided Deep Learning Technique for MRI Image Noise Reduction. Journal of Theory and Practice of Engineering Science, 4(01), 109–117. https://doi.org/10. 53469/jtpes. 2024. 04(01).15

Xie, J. , Pun, C. M. , Pan, Z. , Gao, H. , & Wang, B. (2019). Automatic medical image registration based on an integrated method combining feature and area information. Neural processing letters, 49(1), 263-284.

Feng, H. , Zhang, P. , Xu, X. , Hao, P. , & Chen, W. (2019). An unsupervised suggestive annotation algorithm for 3d ct image processing. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/ Journal of Computer-Aided Design and Computer Graphics, 31 (2), 183-189.

Kim, J. , & Kang, S. (2021). Model-agnostic post-processing based on recursive feedback for medical image segmentation. IEEE Access, 2021(99), 1-1.

Jia, Q., Cui, J., Xiao, Y., Liu, C., Rashid, P., & Gehringer, E. F. (2021). All-in-one: Multi-task learning BERT models for evaluating peer assessments. arXiv.

Wang, X. , Gu, L. , & Wang, Z. (2020). Computer medical image segmentation based on neural network. IEEE Access, 2020 (99), 1-1.

Lahiani, A. , Gildenblat, J. , Klaman, I. , Navab, N. , & Klaiman, E. (2019). Generalizing multistain immunohistochemistry tissue segmentation using end-to-end color deconvolution deep neural networks. IET Image Processing, 13(7), 1066-1073.

Ren, S. , Wang, Y. , Hu, R. , Zuo, L. , & Zhao, H. (2021). Automated segmentation of left ventricular myocardium using cascading convolutional neural networks based on echocardiography. AIP Advances, 11(4), 045003.

Wang, X. , Zhong, X. , Xia, M. , Jiang, W. , Huang, X. , & Gu, Z. , et al. (2019). Automatic carotid artery detection using attention layer region-based convolution neural network. International Journal of Humanoid Robotics, 16(04), 1097-1105.

Weimin WANG, Yufeng LI, Xu YAN, Mingxuan XIAO, & Min GAO. (2024). Enhancing Liver Segmentation: A Deep Learning Approach with EAS Feature Extraction and Multi-Scale Fusion. International Journal of Innovative Research in Computer Science & Technology, 12(1), 26–34. Retrieved from https://ijircst. irpublications. org/index. php/ijircst/ article/ view/21.

Zhou, S. , Nie, D. , Adeli, E. , Yin, J. , & Shen, D. (2019). High-resolution encoder-decoder networks for low-contrast medical image segmentation. IEEE Transactions on Image Processing, 2019 (99), 1-1.

Hao Xu, Shuang Song, and Ze Mao. 2023. Characterizing the Performance of Emerging Deep Learning, Graph, and High Performance Computing Workloads Under Interference. arXiv:2303.15763

Dai, W., Tao, J., Yan, X., Feng, Z., & Chen, J. (2023, November). Addressing Unintended Bias in Toxicity Detection: An LSTM and Attention-Based Approach. In 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 375-379). IEEE.

Li, Z., Zhu, H., Liu, H., Song, J., & Cheng, Q. (2024). Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection. arXiv preprint arXiv: 2403. 02232.

Yufeng Li, Weimin Wang, Xu Yan, Min Gao, & MingXuan Xiao. (2024). Research on the Application of Semantic Network in Disease Diagnosis Prompts Based on Medical Corpus. International Journal of Innovative Research in Computer Science & Technology, 12(2), 1–9. Retrieved from https://ijircst.irpublications.org/index.php/ijircst/article/view/29.

Wang, X. S., Turner, J. D., & Mann, B. P. (2021). Constrained attractor selection using deep reinforcement learning. Journal of Vibration and Control, 27(5-6), 502-514.

Xu, H., Wen, S., Gimenez, A., Gamblin, T., & Liu, X. (2017, May). DR-BW: identifying bandwidth contention in NUMA architectures with supervised learning. In 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 367-376). IEEE.

Diao, S., Luo, W., Hou, J., Lambo, R., Al-Kuhali, H. A., Zhao, H., ... & Qin, W. (2023). Deep multi-magnification similarity learning for histopathological image classification. IEEE Journal of Biomedical and Health Informatics, 27(3), 1535-1545.

Xiao, Y., Wang, T., Sun, X., Li, Y., Song, Y., Cui, J., ... & Gehringer, E. F. (2022, January). Modeling review helpfulness with augmented transformer neural networks. In 2022 IEEE 16th International Conference on Semantic Computing (ICSC) (pp. 83-90). IEEE.

Yuan, S. (2022). Image processing method based on fgca and artificial neural network. Scientific programming, 2022(19), 2022.

Liu, C., Cui, J., Shang, R., Xiao, Y., Jia, Q., & Gehringer, E. (2022). Improving Problem Detection in Peer Assessment through Pseudo-Labeling Using Semi-Supervised Learning. International Educational Data Mining Society.

Diao, S., Zhou, W., Qin, C., Liao, J., Huang, J., Yang, W., & Yao, J. (2023, October). An Unsupervised Multispectral Image Registration Network for Skin Diseases. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 720-729). Cham: Springer Nature Switzerland.